Bioinformatics and Biostatistics Core

生物信息学和生物统计学核心

• 批准号: 8935163
• 负责人: KEI-HOI CHEUNG
• 金额: $ 34.27万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-23 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8935163
• 关键词: Benchmarking; Bioinformatics; Biology; Biometry; Biostatistics Core; Collaborations; Communities; Complement; Complementary DNA; Computational Biology; Data; Data Analyses; Data Quality; Data Set; Data Sources; Databases; Development; Doctor of Philosophy; Event; Experimental Designs; Future; Genomics; Goals; High Performance Computing; Human; Imagery; Individual; Label; Libraries; Link; Messenger RNA; Methods; Modeling; National Institute of Drug Abuse; Nature; Network-based; Neurosciences; Online Systems; Ontology; Pathway Analysis; Peptides; Postdoctoral Fellow; Protein Isoforms; Proteins; Proteome; Proteomics; RNA; RNA Editing; Research; Research Design; Research Personnel; Rodent; Sampling; Sequence Alignment; Source; Speed; Tissues; Training and Education; Transcript; Validation; Variant; Work; analytical method; base; cell type; complement pathway; computing resources; data visualization; database query; differential expression; graduate student; information framework; meetings; member; multidisciplinary; multiple reaction monitoring; next generation; nonhuman primate; novel; open source; programs; protein aminoacid sequence; protein expression; repository; research study; tool; transcriptome sequencing; transcriptomics; web interface

Bioinformatics and Biostatistics Core (BBC): Project Summary 1. Our bioinformatics effort will support the overall goals of the Center via the following. a) Integrate proteomic, transcriptomic, and genomic data to allow isoform-level interrogation of the proteome. b) Provide an isoform-centric database of mRNA and protein abundance in rodent, and extend to non- human primate, and human species. c) Integrate data across individuals, conditions, and species through the development of multi-level network analyses to complement pathway-level analyses. 2. The proposed biostatistics efforts include the following components. a) Provide statistical guide for current and future experimental design, sample quality assessment, exploratory analysis, and visualization for proteomics data including label-free, multiple reaction monitoring, and data-independent acquisition. b) Develop a downstream statistical analysis framework for MS/proteomics data that includes data normalization and significance analysis of differentially expressed proteins or peptides. c) Construct an automated web-based analysis pipeline in collaboration with the YPED team. 3. We will make the following improvements to the Yale Protein Expression Database (YPED). a) Incorporate into the web interface new types of proteomics data and associated data analyses. b) Collaborate with the Bioinformatics and Biostatistics teams to incorporate new data results obtained using their new analysis pipelines for LC-MRM, SWATH and RNA-Seq. c) Link YPED to external data sources via interoperation with Neuroscience Information Framework (NIF). Use NIF ontologies to standardize YPED data annotation and facilitate integration with RNA-seq data. d) Expand the YPED repository (the public portion of YPED) to enable more rapid dissemination of a wider variety of types of proteomics data to the scientific community. 4. The high performance computing (HPC) resource provides the following support. a) Provide continued support of large-scale peptide sequence alignment and support novel pipelines to integrate genomic, transcriptomic, and proteomic datasets. b) Work closely with the database, bioinformatics, and biostatistics teams to help benchmark, scale, optimize, and speed up computing tasks involving large-scale MS data. c) Develop open-source proteomics pipelines (e.g., Skyline) in HPC settings. 5. Training and Education of Graduate students and Postdoctoral Fellows.

生物信息学和生物统计学核心(BBC)：项目摘要 1.我们的生物信息学工作将通过以下方式支持中心的总体目标。 A)整合蛋白质组、转录组和基因组数据，以实现对 蛋白质组。 B)提供以异构体为中心的啮齿动物信使核糖核酸和蛋白质丰度数据库，并扩展到非 人类灵长类和人类物种。 C)通过多层次的开发，整合个人、条件和物种之间的数据 网络分析是对路径水平分析的补充。 2.拟议的生物统计工作包括以下组成部分。 A)为当前和未来的试验设计、样品质量评估、 蛋白质组学数据的探索性分析和可视化，包括无标记、多反应 监控和独立于数据的获取。 B)为MS/蛋白质组学数据制定下游统计分析框架，其中包括数据 差异表达蛋白质或多肽的归一化和显著性分析。 C)与环境政策研究所团队合作，建立一个基于网络的自动化分析渠道。 3.我们将对耶鲁大学蛋白质表达数据库(PYED)进行以下改进。 A)将新类型的蛋白质组学数据和相关数据分析纳入网络界面。 B)与生物信息学和生物统计小组协作，纳入获得的新数据结果 使用他们的新分析管道进行LC-MRM、SWATH和RNA-Seq。 C)通过与神经科学信息框架(NIF)的互操作连接到外部数据源。 使用NIF本体来标准化输入的数据注释，并促进与RNA-seq数据的集成。 D)扩大公共信息库(公共信息库)，以便更快地传播更广泛的 向科学界提供各种类型的蛋白质组学数据。 4.高性能计算(HPC)资源支持如下。 A)继续支持大规模多肽序列比对，并支持新的管道以 整合基因组、转录组和蛋白质组数据集。 B)与数据库、生物信息学和生物统计小组密切合作，帮助制定基准、扩大规模、 优化和加快涉及大规模MS数据的计算任务。 C)在高性能计算环境中开发开源蛋白质组学管道(如Skyline)。 5.研究生和博士后研究员的培训和教育